Patrick R. Mullen, Daniel Gutierrez
The burnout and stress experienced by school counselors is likely to have a negative influence on the services they provide to students, but there is little research exploring the relationship among these variables. Therefore, we report findings from our study that examined the relationship between practicing school counselors’ (N = 926) reported levels of burnout, perceived stress and their facilitation of direct student services. The findings indicated that school counselor participants’ burnout had a negative contribution to the direct student services they facilitated. In addition, school counselors’ perceived stress demonstrated a statistically significant correlation with burnout but did not contribute to their facilitation of direct student services. We believe these findings bring attention to school counselors’ need to assess and manage their stress and burnout that if left unchecked may lead to fewer services for students. We recommend that future research further explore the relationship between stress, burnout and programmatic service delivery to support and expand upon the findings in this investigation.
Keywords: burnout, stress, school counselors, student services, service delivery
The American School Counselor Association (ASCA; 2012) recommends that school counselors enhance the personal, social, academic and career development of all students through the organization and facilitation of comprehensive programmatic counseling services. Delivery of student services is part of a larger framework articulated by ASCA’s National Model (2012) that also includes management, accountability and foundation components of school counseling programs. However, ASCA notes that school counselors should “spend 80 percent or more of their time in direct and indirect services to students” (ASCA, 2012, p. xii). ASCA defines indirect student services as services that are in support of students and involve interactions (e.g., referrals, consultations, collaborations and leadership) with stakeholders other than the student (e.g., parents, teachers and community members). On the other hand, direct student services are interactions that occur face-to-face and involve the facilitation of curriculum (e.g., classroom guidance lessons), individual student planning and responsive services (e.g., individual, group and crisis counseling). In either case, ASCA charges school counselors with prioritizing the delivery of student services.
As a part of their work, school counselors often incur high levels of stress that may result from multiple job responsibilities, role ambiguity, high caseloads, limited resources for coping and limited clinical supervision (DeMato & Curcio, 2004; Lambie, 2007; McCarthy, Kerne, Calfa, Lambert, & Guzmán, 2010). In addition, burnout can result from the ongoing experience of stress (Cordes & Dougherty, 1993; Maslach, 2003; Schaufeli & Enzmann, 1998) and can result in diminished or lower quality rendered services (Lawson & Venart, 2005; Maslach, 2003). While research on burnout is common in the school counseling literature (Butler & Constantine, 2005; Lambie, 2007; Wachter, Clemens, & Lewis, 2008; Wilkerson & Bellini, 2006), studies have not focused on the relationship between burnout and school counselors’ service delivery. Yet, burnout has the potential to produce negative consequences for the work rendered by school counselors and could result in fewer services for students (Lambie, 2007; Lawson & Venart, 2005; Maslach, 2003). Therefore, the purpose of this research was to examine the contribution of school counselors’ levels of burnout and stress to their delivery of direct student services.
School Counselors and the Delivery of Student Services
Research on school counselors’ delivery of student services has produced positive findings. In a meta-analysis that included 117 experimental studies, Whiston, Tai, Rahardja, and Eder (2011) identified that, in general, school counseling services have a positive influence on students’ problem-solving and school behavior. Furthermore, in schools where school counselors completed higher levels of student services focused on improving academic success, personal and social development, and career and college readiness, students experienced a variety of positive outcomes, such as increased sense of belongingness, increased attendance, fewer hassles with other students, and less bullying (Dimmitt & Wilkerson, 2012). Moreover, researchers have shown that the higher occurrence of school counselor-facilitated services is beneficial for students’ educational experience and academic outcomes (Carey & Dimmitt, 2012; Lapan, Gysbers, & Petroski, 2001; Wilkerson, Pérusse, & Hughes, 2013). Overall, the services conducted by school counselors have a positive impact on student success. As such, research investigating the factors related to higher incidence of school counselors’ direct student services could provide significant educational benefits to schools.
Researchers have examined a variety of topics that relate to increased student services. Clemens, Milsom, and Cashwell (2009) found that if school counselors had a good relationship with their principal and were engaged in higher levels of advocacy, they were likely to have increased implementation of programmatic counseling services. Another study concluded that school counselors’ values were not associated with the occurrence of service delivery, but researchers did find counselors with higher levels of leadership practices also delivered more school counseling services (Shillingford & Lambie, 2010). Other factors related to increased levels of school counselors’ service delivery are increased job satisfaction (Baggerly & Osborn, 2006; Pyne, 2011) and higher self-efficacy (Ernst, 2012; Mullen & Lambie, 2016). These studies provided notable contributions to the literature; however, at this time no known studies have examined the relationship among school counselors’ burnout, perceived stress and direct student services.
Stress and Burnout Among School Counselors
Stress is a significant issue that relates to the impairment of work performance (Salas, Driskell, & Hughes, 1996) and is a likely problem for school counselors. The construct of stress has a rich history in scientific literature dating back to the 1930s (Cannon, 1935; Selye, 1936). Selye (1980) articulated one of the first broad definitions of stress by defining it as the “nonspecific results of any demand upon the body” (p. vii). Over time, various authors developed an assortment of definitions (Ivancevich & Matteson, 1980; Janis & Mann, 1977; McGrath, 1976), but Lazarus and Folkman’s (1984) definition of stress is common among scholars (Driskell & Salas, 1996; Lazarus, 2006). In their Transactional Model of Stress and Coping, Lazarus and Folkman (1984) defined stress as a “particular relationship between the person and the environment that is appraised by the person as taxing or exceeding his or her resources and endangering his or her wellbeing” (p. 19). Lazarus and Folkman conceptualized that stress results from an imbalance between one’s perception of demands or threats and their ability to cope with the perceived demands or threats. Consequently, one’s appraisal of demands and their assessment of their coping ability becomes a critical issue in relationship to whether or not the demand will trigger a stress response.
McCarthy et al. (2010) applied Lazarus and Folkman’s model of stress (1984) to school counselors using an instrument that measures the demands and resources experienced by school counselors called the Classroom Appraisal of Resources and Demands–School Counselor Version (McCarthy & Lambert, 2008). McCarthy et al. (2010) found that school counselors who reported challenging demands as a part of their job also had higher levels of stress. This finding is troubling considering that school counselors oftentimes encounter ambiguous job duties, inconsistent job roles and conflicts in their job expectations (Burnham & Jackson, 2000; Culbreth, Scarborough, Banks-Johnson, & Solomon, 2005; Lambie, 2007; Scarborough & Culbreth, 2008). An additional concern is that stress occurring over an extended period of time can lead to emotional and physical health problems (Sapolsky, 2004) along with increased likelihood of leaving the profession (DeMato & Curcio, 2004). Fortunately, prior research reveals that school counselors have reported low stress levels (McCarthy et al., 2010; Rayle, 2006). Still, research on school counselors’ stress and its effects on the services they provide is important.
An additional factor that we believe may have an impact on direct student services is burnout. Burnout was first recognized in the 1970s (Freudenberger, 1974; Maslach, 1976) and is considered to have significant consequences for counseling professionals (Butler & Constantine, 2005; Lambie, 2007; Lawson, 2007; Lee et al., 2007). The topic of burnout is common in the literature across many disciplines (Schaufeli, Leiter, & Maslach, 2009) and has been given particular attention in school counseling research (Butler & Constantine, 2005; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006). Freudenberger (1974, 1986) suggested that burnout results from depleted energy and the feelings of being overwhelmed that emerge from the exposure to diverse issues related to helping others, which over time affects one’s attitude, perception and judgment. Pines and Maslach (1978) described burnout as an ailment “of physical and emotional exhaustion, involving the development of negative self-concept, negative job attitude, and loss of concern and feelings for clients” (p. 234). In 1981, the Maslach Burnout Inventory (MBI) was developed as a method to measure one’s experience of burnout in the helping and human service field (Maslach & Jackson, 1981).
More recently, Lee et al. (2007) expanded the measurement of burnout and presented the construct of counselor burnout, which they defined as “the failure to perform clinical tasks appropriately because of personal discouragement, apathy to symptom stress, and emotional/physical harm” (p. 143). Within their model, Lee and associates found that counselor burnout includes the constructs of exhaustion, negative work environment, devaluing clients, incompetence and deterioration in personal life. These constructs correlate with the factors measured by the MBI (Maslach & Jackson, 1981), but provide a definition consistent with the work of school counselors (Gnilka, Karpinski, & Smith, 2015).
Many researchers have explored factors related to school counselor burnout. Overall, scholars have found that school counselors report low levels of burnout (Butler & Constantine, 2005; Gnilka et al., 2015; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006). Nonetheless, researchers also reported that higher collective self-esteem is associated with a higher sense of personal accomplishment and lower emotional exhaustion (Butler & Constantine, 2005), whereas higher levels of ego development are associated with higher personal accomplishment (Lambie, 2007). Moreover, Wilkerson and Bellini (2006) discovered that school counselors who handle stressors with emotion-focused coping are at a higher risk of experiencing burnout symptoms, and Wilkerson (2009) established that school counselors’ emotion-focused coping increases their likelihood of experiencing symptoms of burnout. Yet, there is no research on the connection between school counselors’ burnout and the direct student services they provide despite a high likelihood that burnout is the cause of fewer and deteriorated services for students (Maslach, 2003).
The purpose of this study was to build upon existing literature regarding school counselors’ stress, burnout and their facilitation of direct student services. The guiding research questions were: (a) Do practicing school counselors’ levels of burnout and perceived stress contribute to their levels of service delivery? and (b) Do practicing school counselors’ levels of stress correlate with their burnout? Consequently, the following research hypotheses were examined: (a) School counselors’ degree of burnout and perceived stress contributes to their facilitation of direct student services, and (b) School counselors’ degree of perceived stress correlates positively with their level of burnout.
Method
Procedures
To answer the research questions associated with this study, we employed a cross-sectional research design (Gall, Gall, & Borg, 2007). Furthermore, this study utilized online survey data collection procedures. Prior to any data collection, we received approval from the Institutional Review Board at the first author’s university. During the first step in the data collection process, we retrieved the name and e-mail address of every school counselor listed in the ASCA online directory of membership. Next, we generated a simple random sample of school counselors. Then, we sent the sample selected from the ASCA online directory a series of three e-mails that aligned with tailored design method (Dillman, Smyth, & Christian, 2009) recommendations for survey research. Each e-mail contained a brief description of the survey and a link to the online survey managed by Qualtrics (2013). If a participant wished to take the survey, he or she was directed to the Web site that posted the explanation of the study. If they agreed to participate, they would move forward and complete the survey. Participants were screened as to whether they were practicing school counselors or not (e.g., student, counselor educator or retired). Of the 6,500 participants sampled, 41 indicated they were not a practicing school counselor. In addition, 312 e-mails were not working at the time of the survey. Out of the 6,147 practicing school counselors surveyed, 1,304 (21.21% visit response rate) visited the survey Web site and 926 completed the survey in its entirety, which resulted in a 15.06% useable response rate. The response rate received for this study is high in comparison to studies using similar methods (e.g., 14%, Harris, 2013; 11.4%, Mullen, Lambie & Conley, 2014).
Participant Characteristics
Participants (N = 926) were practicing school counselors in private, public and charter K–12 educational settings from across the United States. The mean age was 43.27 (SD = 10.03) and included 816 (88.1%) female and 110 (11.9%) male respondents. The participants’ ethnicity included 50 (5.4%) African Americans, 5 (.5%) Asian Americans, 29 (3.1%) Hispanic Americans, 11 (1.2%) Multiracial, 2 (.2%) Native Americans, 4 (.4%) Pacific Islanders, 811 (87.6%) European Americans, and 13 (1.5%) participants who identified their ethnicity as “Other.” On average, participants had 10.97 (SD = 6.92) years of experience and 401.45 (SD = 262.05) students on their caseload. The geographical location of the participants’ work setting favored suburban (n = 434, 46.9%) and rural communities (n = 321, 34.7%) with fewer school counselors working in urban settings (n = 171, 18.5%). Most participants reported that they worked in the high school grade levels (n = 317, 34.2%) closely followed by elementary (n = 270, 29.2%) and middle school or junior high school (n = 203, 21.9%) grade levels, with 136 (14.7%) respondents working in another grade level format (e.g., grades K–12, K–8, or 6–12).
Measures
This study used the (a) Counselor Burnout Inventory (CBI; Lee et al., 2007), (b) the School Counselor Activity Rating Scale (SCARS; Scarborough, 2005), and (c) the Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983). Participants also completed a researcher-created demographics form regarding their personal characteristics (e.g., age, gender and ethnicity) and work-related characteristics (e.g., location type, grade level, caseload, experience as a school counselor and percentage of time they directly work with students).
CBI. The CBI (Lee et al., 2007) is a 20-item self-report measure that examines counselor burnout across five domains. The domains that make up the CBI include: (a) exhaustion, (b) incompetence, (c) negative work environment, (d) devaluing client, and (e) deterioration in personal life. The CBI makes use of a 5-point Likert rating scale that ranges from 1 (never true) to 5 (always true) and examines emotional states and behaviors representative of burnout. Some sample items include “I feel exhausted due to my work as a counselor” (exhaustion), “I feel I am an incompetent counselor” (incompetence), “I feel negative energy from my supervisor” (negative work environment), “I have little empathy for my clients” (devaluing client), and “I feel I have poor boundaries between work and my personal life” (deterioration in personal life). Lee et al. (2007) demonstrated the construct validity of the CBI through an exploratory factor analysis that identified a five-factor solution in addition to a confirmatory factor analysis that supported the five-factor model with an adequate fit to the data.
Gnilka et al. (2015) found support for the five-factor structure of the CBI (Lee et al., 2007) with school counseling using confirmatory factor analysis, which supports the CBI as an appropriate measure for school counselor burnout. Lee et al. (2007) established convergent validity for the CBI based upon the correlations between the subscales on the Maslach Burnout Inventory-Human Services Survey (Maslach & Jackson, 198l) and the CBI. In prior research, the Cronbach’s alphas of the CBI subscales indicated good internal consistency (Streiner, 2003) with score ranges of .80 to .86 for exhaustion, .73 to .81 for incompetence, .83 to .85 for negative work environment, .61 to .83 for devaluing client, and .67 to .84 for deterioration in personal life (Lee et al., 2007; Lee, Cho, Kissinger, & Ogle, 2010; Puig et al., 2012). The internal consistency coefficients of the CBI in this investigation also were good (Streiner, 2003) with Cronbach’s alphas of .87 for exhaustion, .79 for incompetence, .84 for negative work environment, .79 for devaluing client, and .81 for deterioration in personal life.
SCARS. The SCARS (Scarborough, 2005) is a 48-item verbal frequency measure that examines the occurrence that school counselors actually perform and prefer to perform components of the ASCA National Model (2012). The SCARS measures school counselors’ ratings of activities based on the four levels of interventions articulated by ASCA (1999) and the ASCA National Model (2003). Unfortunately, a more recent version of the SCARS that articulates the new ASCA National Model (2012) does not exist. Nevertheless, this study utilized two SCARS scales (counseling and curriculum) that measure the incidence of direct student services. To the benefit of this investigation, the direct services measured on the SCARS have not changed in the new edition of the ASCA National Model (2003, 2012). Similar to Shillingford and Lambie (2010) and Mullen and Lambie (2016), this investigation utilized the actual scale, but not the prefer scale, on the SCARS (Scarborough, 2005) because this study sought to examine the frequency that school counselors delivered direct student services, not their preferences and not the difference between their preference and actuality. The subscales that measure direct student services used in this study included the counseling (e.g., group and individual counseling interventions; 10 items) and curriculum (e.g., classroom guidance interventions; 8 items) subscales, whereas the coordination, consultation and other activities scales were not used because they measure indirect activities.
The SCARS (Scarborough, 2005) assesses the frequency of school counselor service delivery with a 5-point Likert rating scale that ranges from 1 (I never do this) to 5 (I routinely do this). Scores on the SCARS can be total scores or mean scores. Some sample items from the counseling subscale are “Counsel with students regarding school behavior” and “Provide small group counseling for academic issues.” Some sample items from the curriculum subscale are “Conduct classroom lessons addressing career development and the world of work” and “Conduct classroom lessons on conflict resolution.” Scarborough (2005) examined the validity by investigating the variances in score on the actual scale based on participant grade level and found that participants’ grade level had a statistically significant effect across the scales with small to large effect sizes (e.g., ranging from .11 to .68[ω2]), which supported the convergent validity of the SCARS. Additionally, construct validity was supported using factor analysis. In prior research using the SCARS, the internal consistency of the counseling and curriculum scales was strong with Cronbach’s alphas of .93 for the curriculum actual scale and .85 for the counseling actual scale (Scarborough, 2005). The internal consistency coefficients of the SCARS actual subscales in this investigation were good (Streiner, 2003) with Cronbach’s alphas of .77 for the counseling scale and .93 for the curriculum scale.
PSS. The PSS (Cohen et al., 1983) is a 10-item self-report measure that examines the participants’ appraisal of stress by asking about feelings and thoughts during the past month. The PSS uses a 5-point Likert scale that ranges from 0 (never) to 4 (very often) and includes four positively stated items that are reverse coded. Some sample items include, “In the last month, how often have you felt that you were on top of things?” (reverse coded), and “In the last month, how often have you been upset because of something that happened unexpectedly?” The PSS has been shown to have acceptable internal consistency with Cronbach’s alphas ranging from .84 to .91 (Chao, 2011; Cohen et al., 1983; Daire, Dominguez, Carlson, & Case-Pease, 2014). The internal consistency coefficient of the PSS in this study also was acceptable (Streiner, 2003) with a Cronbach’s alpha of .88.
Results
Preliminary Analysis
Initial screening of the data included the search for outliers (e.g., data points three or more standard deviations from the mean) using converted z-scores (Osborne, 2012), which resulted in identifying 21 cases that had at least one variable with an extreme outlier. To accommodate for these outliers, the researchers utilized a Windorized mean based on adjacent data points (Barnett & Lewis, 1994; Osborne & Overbay, 2004). Next, the assumptions associated with structural equation modeling (SEM) were tested (e.g., normality and multicollinearity; Hair, Black, Babin, Anderson, & Tatham, 2006; Tabachnick & Fidell, 2007). Multicollinearity was not present with these data; however, the data violated the assumption of normality of a single composite variable (e.g., devaluing clients scale on the CBI). Researchers conducted descriptive analyses of the data using the statistical software SPSS. Table 1 presents the means, standard deviations and correlations for the study variables.
Model Testing
This correlational investigation utilized a two-step SEM method (Kline, 2011) to examine the research hypothesis employing AMOS (version 20) software. The first step included a confirmatory factor analysis (CFA) to inspect the measurement model of burnout and its fit with the data. Then, a structural model was developed based on the measurement model. The measurement model and structural model were appraised using model fit indices, standardized residual covariances, standardized factorial loadings and standardized regression estimates (Byrne, 2010; Kline, 2011). Modifications to the models were made as needed (Kline, 2011). Both the measurement and the structural models employed the use of maximum likelihood estimation technique despite the presence of non-normality based on recommendations from the literature (Curran, West, & Finch, 1996; Hu, Bentler, & Kano, 1992; Lei & Lomax 2005; Olsson, Foss, Troye, & Howell, 2000).
| Table 1 Correlations among measures of direct student services, perceived stress, and burnout | |||||||||||
|
M |
SD |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
| Counseling |
3.02 |
.60 |
– |
||||||||
| Curriculum |
2.77 |
1.16 |
.44 |
– |
|||||||
| Percent of Time |
59 |
78 |
.36 |
.27 |
– |
||||||
| Perceived Stress |
1.56 |
.63 |
-.15 |
-.11 |
-.14 |
– |
|||||
| Exhaustion |
3.04 |
.86 |
-.15 |
-.11 |
-.11 |
.61 |
– |
||||
| Incompetence |
2.29 |
.68 |
-.31 |
-.14 |
-.18 |
.49 |
.44 |
– |
|||
| NEW |
2.56 |
.87 |
-.23 |
-.19 |
-.22 |
.46 |
.53 |
.39 |
– |
||
| DC |
1.39 |
.50 |
-.20 |
-.17 |
-.14 |
.32 |
.28 |
.45 |
.64 |
– |
|
| DPL |
2.39 |
.80 |
-.19 |
-.12 |
-.16 |
.58 |
.66 |
.41 |
.47 |
.30 |
– |
| Note. N = 926. All correlations (r) were statistically significant (p < .001). Counseling = frequency of direct counseling services, curriculum = frequency of direct curriculum services, percent of time = percent of time in direct services to students, NEW = negative work environment, DC = devaluing client, DPL = deterioration in personal life. | |||||||||||
Multiple fit indices were examined to determine the goodness of fit for the measurement model and structural model (Hu & Bentler, 1999; Kline, 2011; Weston & Gore, 2006). The fit indices that were used include: (a) chi-square, (b) comparative fit index (CFI), (c) goodness of fit (GFI), (d) standardized root mean square residual (SRMSR), and (e) root mean square error of approximation (RMSEA). Furthermore, we consulted the normed fit index (NFI) and Tucker-Lewis index (TLI) because they are more robust to non-normal data as compared to other indices (Lei & Lomax, 2005). For a detailed description of these fit indices, readers can review the works of Hu and Bentler (1999), Kline (2011), and Weston and Gore (2006). We used these fit indices to establish a diverse view of model fit.
Measurement model. First, we employed a CFA model to examine the latent variable representing burnout (Lee et al., 2007). The research team totaled each subscale on the CBIs to develop a composite score for each domain. The initial measurement model for burnout produced acceptable standardized factor loadings ranging from .41 (devaluing client) to .57 (incompetence), .62 (negative work environment), .77 (deterioration in personal life), and .82 (exhaustion). Furthermore, all fit indices for the measurement model indicated an adequate fitting model except chi-square, RMSEA, and TLI: χ2 (df = 5, N = 926) = 107.07, p < .001; GFI = .96; CFI = .92; RMSEA = .15; SRMR = .06; NFI = .92; TLI = .85. Therefore, we consulted the modification indices and standardized residual covariance matrix and tested a new CFA based upon these consultations.
The modifications indices indicated the need to correlate the error terms for incompetence and devaluing client. The resulting model produced a model in which all fit indices indicated an adequate fitting model: χ2 (df = 4, N = 926) = 12.03, p = .02; GFI = .99; CFI = .99; RMSEA = .05; SRMR = .02; NFI = .99; TLI = .99. Further inspection of the standardized factor loadings for the model indicated they were all acceptable except for the factor loading for devaluing client, which dropped to .36 (below .40; Stevens, 1992). While these modifications improved the overall fit of the CFA, the correlation of incompetence and devaluing client has no theoretical justification (Byrne, 2010). In addition, the correlation of the error terms for incompetence and devaluing client produced a standardized factor loading below the noted standard of .40 (Kline, 2011; Stevens, 1992). Subsequently, we removed the subscale of devaluating client given: (a) the low factor loading produced after modification of the initial model, and (b) the lack of normality in the composite score.
Next, we examined the new modified measurement model that included the removal of the subscale devaluing client. The resulting model (see Figure 1) produced a model in which all fit indices indicated a good fitting model: χ2 (df = 2, N = 926) = 8.25, p = .02; GFI = .99; CFI = .99; RMSEA = .06; SRMR = .02; NFI = .99; TLI = .98. The modified measurement model for burnout produced acceptable standardized factor loadings ranging from .53 (incompetence) to .63 (negative work environment), .77 (deterioration in personal life), and .85 (exhaustion). In review of the model fit indices and standardized factor loadings, we deemed the measurement model acceptable for use in the structural model.
Structural model. We developed the structural model (see Figure 1) based on a review of the literature, and it was theorized in this model that school counselors’ perceived stress correlates to school counselors’ burnout and contributes to the frequency with which they provide direct student services. In addition, this model tested the hypothesized model that school counselors’ burnout contributes to their frequency of direct student services. The structural model includes the measurement model previously tested that consisted of the latent variable of burnout. School counselors’ perceived stress and burnout were defined as exogenous or independent variables. Perceived stress was a manifest variable consisting of participants’ composite scores on the PSS (Cohen et al., 1983).
Additionally, we defined the manifest variables of percentage of time at work providing direct services to students, direct curriculum activities, and direct counseling activities as the endogenous or dependent variables that measure participants’ facilitation of direct student services. The variable of percentage of time at work providing direct services to students was a single demographic item reported by participants, while direct curriculum activities and direct counseling activities were the participants’ composite scores derived from subscales on the SCARS (Scarborough, 2005). In addition, the error terms of the direct student services variables—percentage of time at work providing direct services to students, direct curriculum activities and direct counseling activities—were correlated given that they measure similar constructs.
An examination of the structural model indicated a strong goodness of fit for all fit indices except for chi-square: χ2 (df = 14, N = 926) = 108.37, p < .001; GFI = .97; CFI = .96; RMSEA = .07; SRMR = .04; NFI = .95; TLI = .91. The researchers deemed the structural model as suitable with these data despite the significant chi-square (Henson, 2006; Kline, 2011; Weston & Gore, 2006). A closer examination of the standardized regression weights identified that school counselors’ burnout scores contributed to 12% (β = -.35, p < .001) of the variance in their direct counseling activities and 5% (β = -.22, p < .001) of the variance in their direct curriculum activities. Furthermore, school counselors’ burnout scores contributed to 6% (β = -.24, p < .001) of the variance in percentage of time at work providing direct services to students. Perceived stress did not contribute to direct counseling activities (β = .11, p = .04), direct curriculum activities (β = .06, p = .31), and percentage of time at work providing direct services to students (β = .04, p = .51). In addition, perceived stress and burnout produced a statistically significant correlation (β = .75, p < .001; 56% of the variance explained).
The structural model (Figure 1) indicates that school counselors’ level of counselor burnout had a negative contribution to the frequency of their direct counseling activities, direct curriculum activities and percentage of time at work providing direct services to students. However, it should be noted that the effect sizes of these findings were small to medium (Sink & Stroh, 2006). An additional finding from this investigation was that the perceived stress correlated with burnout with a large effect size (Sink & Stroh, 2006); however, perceived stress did not have a statistically significant contribution to school counselors’ direct counseling activities, direct curriculum activities, and percentage of time at work providing direct services to students.
Figure 1. Final hypothesized structural model depicting the relationship between school counselors’ (N = 926) perceived stress, burnout, and direct student services.
Discussion
This study examined the relationship between school counselors’ reported burnout, perceived stress and frequency of direct student services. The findings indicated burnout was a statistically significant contributor to the frequency of direct counseling services (β = -.35; medium effect size) and direct curriculum services (β = -.22; small to medium effect size). Furthermore, the findings identified that burnout was a significant contributor to the participants’ report of the percentage of time they spend on their job working directly with students (β = -.24; small to medium effect size). Although the results should be interpreted with some level of caution, we found that burnout also had a statistically significant relationship to frequency of direct student services with increased levels of burnout relating to lower levels of direct student services. Nonetheless, these findings are not surprising considering the literature on burnout emphasizes the important role burnout plays on the effort one places on their job, with individuals presenting with higher burnout typically having lower investment interest in their job (Garman, Corrigan, & Morris, 2002; Landrum, Knight, & Flynn, 2012; Maslach, 2003). While the findings support the literature on the role of burnout, they also bring attention to the possibility that burnout does not have a strong relationship to school counselors’ facilitation of direct counseling services as noted by the small effect size.
An interesting finding was that school counselors’ degree of perceived stress did not contribute to the direct student services variables and yet did correlate with burnout. In fact, the relationship between perceived stress and counselor burnout had a large effect size, with 56% of the variance among these variables explained by their relationship. This finding accentuates the difference between the constructs of burnout and stress because burnout had a statistically significant relationship with the direct student services variables and stress did not, despite the strength of the relationship between burnout and stress. One interpretation of this finding is that school counselors’ ability to manage and cope with stress permits them to complete their job functions, whereas burnout may be more challenging to overcome. Furthermore, scholars state that prolonged exposure to stress worsens or cultivates burnout (Cordes & Dougherty, 1993; Schaufeli & Enzmann, 1998). This finding is logical given the theory behind burnout (Lee et al., 2007; Maslach, 2003); yet, this is one of only a few studies (McCarthy et al., 2010; Wilkerson & Bellini, 2006) in the school counseling literature to examine this relationship. However, these results need further exploration. As McCarthy et al. (2010) noted, the construct of stress is multidimensional (includes appraisal of resources and demands) and the PSS (Cohen et al., 1983) is a single-dimension scale. Therefore, a scale that examines stress in a multifaceted manner may produce different results.
An additional finding worth discussion involves the measurement model of the CBI (Lee et al., 2007). Specifically, this study found that the construct of devaluing client did not fit with the data. Furthermore, participants reported low scores regarding the devaluing client scale, as indicated by the descriptive statistics. The devaluing client subscale also was the only subscale on the CBI that was not normally distributed. These results were similar to Gnilka et al.’s (2015) findings that indicated school counselors are likely to maintain high levels of empathy and positive regard for their students. These findings may indicate that the devaluing clients subscale may not reflect symptoms of burnout for school counselors. This is a promising finding as it suggests that school counselors do not develop a negative perspective of students because of the negative consequences of their job.
The descriptive statistics from this investigation also provide some noteworthy information. First, participants reported moderate to low levels of burnout across the five factors of the CBI (Lee et al., 2007), with exhaustion having the highest mean score. These results are consistent with prior research (Butler & Constantine, 2005; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006) on burnout and indicate that, overall, school counselors report low levels of burnout. An additional finding was that school counselors reported a low level of perceived stress, which is surprising given the challenge of role ambiguity, confusion and conflict (Burnham & Jackson, 2000; Culbreth et al., 2005; Lambie, 2007; Scarborough & Culbreth, 2008). However, school counselors have reported low levels of stress in other research (e.g., McCarthy et al., 2010; Rayle, 2006). The last noteworthy finding from the descriptive statistics was the measures of direct student services. This investigation was one of the first to focus specifically on the topic of direct student services versus other aspects of school counselors’ roles. This study found that school counselors reported that, on average, they spend over half their time working directly with students. In addition, they reported high frequencies for facilitating both curriculum and counseling activities. These findings are promising and consistent with other research examining these constructs (Mullen & Lambie, 2016; Scarborough & Culbreth, 2005; Shillingford & Lambie, 2010). Overall, the results from this study provide new and novel information for the school counseling discipline.
Limitations and Implications for Future Research
Readers should interpret these findings within the context of their limitations. Some limitations from this study include: (a) associational research using correlation statistics does not establish cause and effect relationships; (b) the response rate, although high as compared to other studies with similar methods, is low; and (c) the generalizability of these findings is limited by the sampling procedures (e.g., only sampled ASCA members; Gall et al., 2007). In addition, participants who respond to surveys may have different characteristics as compared to those school counselors who chose not to participate (Gall et al., 2007).
The findings from this study have implications for future research. A prominent direction for future research is the examination of the relationship between stress and programmatic service delivery, including direct student services. This study identified that perceived stress has no relationship with direct service delivery, but a multidimensional measure of stress (McCarthy & Lambert, 2008) may produce different results. Similarly, this study found that perceived stress relates to higher levels of burnout and supports the theory that chronic stress relates to increased burnout. Future research might further confirm these findings.
Another relevant future research implication is exploring factors that prevent or mediate the contribution of burnout to school counselor service delivery, considering this investigation found a significant relationship between these constructs. A variety of mechanisms may serve as buffers between burnout and programmatic service delivery, such as coping skills, career-sustaining behaviors, emotional intelligence, grit, or self-efficacy. Nonetheless, the identification of preventative skills or personal traits that inhibit the effects of burnout may lead to interventions to support school counselors’ work. Future research also can examine training interventions that target school counselors’ susceptibility to burnout or stress. A final research implication is the need to replicate and confirm our findings. Researchers might consider replicating this study with similar or different measures and data collection methods.
Implications for School Counseling Practitioners and Supervisors
The degree of perceived stress for participants in this study had a positive correlation with their degree of burnout. Furthermore, participants’ burnout negatively contributed to their level of direct student services. While this study included several limitations, these findings provide more evidence for the positive relationship between stress and burnout, in addition to the negative contribution burnout can have on the job functions of school counselors. In an effort to support direct student services, it would behoove school counselors to take steps to increase their awareness about their well-being, including symptoms of burnout, and seek support to address concerns as they arise. Additionally, school counselors’ failure to address burnout is an ethical concern (American Counseling Association, 2014). School counselors could utilize a self-assessment (i.e., Counselor Burnout Inventory [Lee et al., 2007] or Professional Quality of Life Scale [Stamm, 2010]) to examine their level of burnout and subsequently address their work functions and lifestyle to alleviate symptoms.
As Moyer (2011) pointed out, supervision plays a vital role in school counselor development and can be a way to alleviate burnout. Thus, supervisors can provide opportunities for school counselors to learn ways to assess their well-being with the aim of developing career-sustaining behaviors to prevent burnout. For example, supervisors can inform school counselors of available screening measures and provide resources to aid in the development of career-sustaining behaviors. Similarly, supervisors can create activities (Lambie, 2006) that assess school counselors’ well-being, which allows counselors to address negative feelings. Efforts made to prevent burnout may increase the chances of school counselors performing direct student services. Higher rates of direct student services, such as individual and group counseling, also may lead to better educational outcomes for students (Lapan, 2012).
In an effort to reduce school counselors’ burnout and potentially increase their delivery of direct student services, practitioners and supervisors can initiate wellness-related activities. Butler and Constantine (2005) noted that peer supervision or consultation along with social support from colleagues and administrators might be helpful for reducing the effects of burnout. Furthermore, Lawson and Myers (2011) reported on the highest rated career-sustaining behavior, which provides potential to support the wellness of school counseling practitioners. As Meyer and Ponton (2006) noted, counselors as a whole tend to put their own wellness to the side in order to provide services to their clients. Therefore, another consideration for school districts and school counseling organizations is to offer wellness-focused training that could raise attention to counselors’ level of stress and burnout and provide strategies to enhance their wellness. Additionally, school counselors should remember to advocate for the profession and for themselves (Young & Lambie, 2007). It is important that administrators understand the critical wellness needs of school counselors, and school counselors should be among the first to advocate for this cause. As these findings indicate, there is a relationship between burnout and the quality of services offered by school counselors. Therefore, it is important that counselors “learn to be their own advocates and help dysfunctional workplaces become well” (Young & Lambie, 2007, p. 99).
In summary, this study examined the association of practicing school counselors’ degree of burnout, perceived stress and frequency of direct student services. The findings indicated that higher levels of burnout contribute to a decreased frequency of direct student services. Furthermore, school counselors’ perceived stress does not contribute to their facilitation of direct student services, but was positively associated with burnout. Overall, these findings are encouraging because the descriptive statistics indicate that school counselors operate at a low level of burnout and perceived stress and provide a moderate to high frequency of direct student services.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
American Counseling Association. (2014). 2014 ACA code of ethics. Alexandria, VA: Author.
American School Counselor Association. (1999). The role of the professional school counselor. Alexandria, VA: Author.
American School Counselor Association. (2003). The ASCA National Model: A framework for school counseling programs (1st ed.). Alexandria, VA: Author.
American School Counselor Association. (2012). The ASCA National Model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.
Baggerly, J., & Osborn, D. (2006). School counselors’ career satisfaction and commitment: Correlates and predictors. Professional School Counseling, 9, 197–205. doi:10.5330/prsc.18.1.428818712j5k8677
Barnett, V., & Lewis, T. (1994). Outliers in statistical data (Vol. 3). New York, NY: Wiley.
Burnham, J. J., & Jackson, C. M. (2000). School counselor roles: Discrepancies between actual practice and exis-ting models. Professional School Counseling, 4, 41–49.
Butler, S. K., & Constantine, M. G. (2005). Collective self-esteem and burnout in professional school counselors. Professional School Counseling, 9, 55–62. doi:10.5330/prsc.9.1.17n4415l163720u5
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. New York, NY: Routledge.
Cannon, W. B. (1935). Stresses and strains of homeostasis. The American Journal of the Medical Sciences, 189, 13–14.
Carey, J., & Dimmitt, C. (2012). School counseling and student outcomes: Summary of six statewide studies. Professional School Counseling, 16, 146–153. doi:10.5330/PSC.n.2012-16.146
Chao, R. C. (2011). Managing stress and maintaining well-being: Social support, problem-focused coping, and avoidant coping. Journal of Counseling & Development, 89, 338–348.
doi:10.1002/j.1556-6678.2011.tb00098.x
Clemens, E. V., Milsom, A., & Cashwell, C. S. (2009). Using leader-member exchange theory to examine prin-cipal-school counselor relationships, school counselors’ roles, job satisfaction, and turnover intentions. Professional School Counseling, 13, 75–85. doi:10.5330/PSC.n.2010-13.75
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396.
Cordes, C. L., & Dougherty, T. W. (1993). A review and an integration of research on job burnout. The Academy of Management Review, 18, 621–656.
Culbreth, J. R., Scarborough, J. L., Banks-Johnson, A., & Solomon, S. (2005). Role stress among practicing school counselors. Counselor Education and Supervision, 45, 58–71. doi:10.1002/j.1556-6978.2005.tb00130.x
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29.
Daire, A. P., Dominguez, V. N., Carlson, R. G., & Case-Pease, J. (2014). Family Adjustment Measure: Scale construction and validation. Measurement and Evaluation in Counseling and Development, 47, 91–101. doi:10.1177/0748175614522270
DeMato, D. S., & Curcio, C. C. (2004). Job satisfaction of elementary school counselors: A new look. Professional School Counseling, 7, 236–245.
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method (3rd ed.). Hoboken, NJ: Wiley.
Dimmitt, C., & Wilkerson, B. (2012). Comprehensive school counseling in Rhode Island: Access to services and student outcomes. Professional School Counseling, 16, 125–135. doi:10.5330/PSC.n.2012-16.125
Ernst, K. A. (2012). Self-efficacy, attachment, and school counselor service delivery. Unpublished Doctoral Dissertation, George Washington University.
Freudenberger, H. J. (1974). Staff burn-out. Journal of Social Issues, 30, 159–165. doi:10.1111/j.1540-4560.1974.tb00706.x
Freudenberger, H. J. (1986). The issues of staff burnout in therapeutic communities. Journal of Psychoactive Drugs, 18, 247–251. doi:10.1080/02791072.1986.10472354
Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8th ed.). Boston, MA: Pearson.
Garman, A. N., Corrigan, P. W., & Morris, S. (2002). Staff burnout and patient satisfaction: Evidence of relation-ships at the care unit level. Journal of Occupational Health Psychology, 7, 235–241.
doi:10.1037/1076-8998.7.3.235
Gnilka, P. B., Karpinski, A. C., & Smith, H. J. (2015). Factor structure of the Counselor Burnout Inventory in a sample of professional school counselors. Measurement and Evaluation in Counseling and Development, 48, 177–191.
Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson.
Harris, H. L. (2013). A national survey of school counselors’ perceptions of multiracial students. Professional School Counseling, 17, 1–19. doi:10.5330/PSC.n.2013-17.1
Henson, R. K. (2006). Effect-size measures and meta-analytic thinking in counseling psychology research. The Counseling Psychologist, 34, 601–629. doi:10.1177/0011000005283558
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
Hu, L., Bentler, P. M., & Kano, Y. (1992). Can test statistics in covariance structure analysis be trusted? Psycho-logical Bulletin, 112, 351–362.
Ivancevich, J. M., & Matteson, M. T. (1980). Stress and work: A managerial perspective. Glenview, IL: Scott Foresman.
Janis, I. L., & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. New York, NY: Free Press.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
Lambie, G. W. (2006). Burnout prevention: A humanistic perspective and structured group supervision activity. The Journal of Humanistic Counseling, Education and Development, 45, 32–44.
Lambie, G. W. (2007). The contribution of ego development level to burnout in school counselors: Implications
for professional school counseling. Journal of Counseling & Development, 85, 82–88.
doi:10.1002/j.1556-6678.2007.tb00447.x
Landrum, B., Knight, D. K., & Flynn, P. M. (2012). The impact of organizational stress and burnout on client engagement. Journal of Substance Abuse Treatment, 42, 222–230. doi:10.1016/j.jsat.2011.10.011
Lapan, R. T. (2012). Comprehensive school counseling programs: In some schools for some students but not in all schools for all students. Professional School Counseling, 16, 84–88. doi:10.5330/PSC.n.2012-16.84
Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A state-wide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x
Lawson, G. (2007). Counselor wellness and impairment: A national survey. The Journal of Humanistic Counseling, Education and Development, 46, 20–34. doi:10.1002/j.2161-1939.2007.tb00023.x
Lawson, G., & Myers, J. E. (2011). Wellness, professional quality of life, and career-sustaining behaviors: What keeps us well? Journal of Counseling & Development, 89, 163–171. doi:10.1002/j.1556-6678.2011.tb00074.x
Lawson, G., & Venart, B. (2005). Preventing counselor impairment: Vulnerability, wellness, and resilience. In G. R. Walz & R. K. Yep (Eds.), Vistas: Compelling perspectives on counseling 2005 (pp. 243–246). Alexandria, VA: American Counseling Association.
Lazarus, R. S. (2006). Stress and emotion: A new synthesis. New York, NY: Springer.
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York, NY: Springer.
Lee, S. M., Baker, C. R., Cho, S. H., Heckathorn, D. E., Holland, M. W., Newgent, R. A., . . . Yu, K. (2007). Devel-opment and initial psychometrics of the Counselor Burnout Inventory. Measurement and Evaluation in Counseling and Development, 40, 142–154.
Lee, S. M., Cho, S. H., Kissinger, D. and Ogle, N. T. (2010). A typology of burnout in professional counselors. Journal of Counseling & Development, 88, 131–138. doi:10.1002/j.1556-6678.2010.tb00001.x
Lei, M., & Lomax, R. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling, 12, 1–27. doi:10.1207/s15328007sem1201_1
Maslach, C. (1976). Burned-out. Human Behavior, 5(9), 16–22.
Maslach, C. (2003). Burnout: The cost of caring. Cambridge, MA: Malor Books.
Maslach, C., & Jackson, S. E. (1981). MBI research edition manual. Palo Alto, CA: Consulting Psychologists Press.
McCarthy, C., Kerne, V. V. H., Calfa, N. A., Lambert, R. G., & Guzmán, M. (2010). An exploration of school counselors’ demands and resources: Relationship to stress, biographic, and caseload characteristics. Professional School Counseling, 13, 146–158. doi:10.5330/PSC.n.2010-13.146
McCarthy, C. J., & Lambert, R. G. (2008). Counselor appraisal of resources and demands. Charlotte, NC: Center for Educational Measurement and Evaluation.
McGrath, J. E. (1976). Stress and behavior in organizations. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1351–1395). Chicago, IL: Rand McNally.
Meyer, D., & Ponton, R. (2006). The healthy tree: A metaphorical perspective of counselor well-being. Journal of Mental Health Counseling, 28, 189–201. doi:10.17744/mehc.28.3.0341ly2tyq9mwk7b
Moyer, M. (2011). Effects of non-guidance activities, supervision, and student-to-counselor ratios on school counselor burnout. Journal of School Counseling, 9(5). Retrieved from http://jsc.montana.edu/articles/v9n5.pdf
Mullen, P. R., & Lambie, G. W. (2016). The contribution of school counselors’ self-efficacy to their programmatic service delivery. Psychology in the Schools, 53, 306–320.
Mullen, P. R., Lambie, G. W., & Conley, A. H. (2014). Development of the Ethical and Legal Issues in Counsel-
ing Self-Efficacy Scale. Measurement and Evaluation in Counseling and Development, 47, 62–78. doi:10.1177/0748175613513807
Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling, 7, 557–595. doi:10.1207/S15328007SEM0704_3
Osborne, J. W. (2012). Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data. Thousand Oaks, CA: Sage.
Osborne, J. W., & Overbay, A. (2004). The power of outliers (and why researchers should always check for them). Practical Assessment, Research & Evaluation, 9(6). Retrieved from http://PAREonline.net/getvn.asp?v=9&n=6
Pines, A., & Maslach, C. (1978). Characteristics of staff burnout in mental health settings. Psychiatric Services, 29, 233–237. doi:10.1176/ps.29.4.233
Puig, A., Baggs, A., Mixon, K., Park, Y. M., Kim, B. Y., & Lee, S. M. (2012). Relationship between job burnout and personal wellness in mental health professionals. Journal of Employment Counseling, 49, 98–109. doi:10.1002/j.2161-1920.2012.00010.x
Pyne, J. R. (2011). Comprehensive school counseling programs, job satisfaction, and the ASCA National Model. Professional School Counseling, 15, 88–97. doi:10.5330/PSC.n.2011-15.88
Qualtrics. (2013). Qualtrics software (Version 37,892) [Computer software]. Provo, UT: Qualtrics Research Suite.
Rayle, A. D. (2006). Do school counselors matter? Mattering as a moderator between job stress and job satisfac-tion. Professional School Counseling, 9(3), 206–215. doi:10.5330/prsc.9.3.w23j476w45727537
Salas, E., Driskell, J. E., & Hughes, S. (1996). Introduction: the study of stress and human performance. In J. E. Driskell and E. Salas (Eds.), Stress and human performance (pp. 1–46). Mahwah, NJ: Erlbaum.
Sapolsky, R. M. (2004). Why zebras don’t get ulcers (3rd ed.). New York, NY: Holt.
Scarborough, J. (2005). The school counselor activity rating scale: An instrument for gathering process data. Professional School Counseling, 8, 274–283.
Scarborough, J. L., & Culbreth, J. R. (2008). Examining discrepancies between actual and preferred practice of
school counselors. Journal of Counseling & Development, 86, 446–459.
doi:10.1002/j.1556-6678.2008.tb00533.x
Schaufeli, W. B., & Enzmann, D. (1998). The burnout companion to study and practice: A critical analysis. Washington, DC: Taylor & Francis.
Schaufeli, W. B., Leiter, M. P., & Maslach, C. (2009). Burnout: 35 years of research and practice. Career Develop-ment International, 14, 204–220. doi:10.1108/13620430910966406
Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature, 138, 32.
Selye, H. (1980). The stress concept today. In I. L. Kutach & L. B. Schlesinger (Eds.), Handbook on stress and anxiety:
Contemporary knowledge, theory, and treatment (pp. 127–143). San Francisco, CA: Jossey-Bass.
Shillingford, M. A., & Lambie, G. W. (2010). Contribution of professional school counselors’ values and leader-
ship practices to their programmatic service delivery. Professional School Counseling, 13, 208–217. doi:10.5330/PSC.n.2010-13.208
Sink, C. A., & Stroh, H. R. (2006). Practical significance: The use of effect sizes in school counseling research. Pro-fessional School Counseling, 9, 401–411. doi:10.5330/prsc.9.4.283746k664204023
Stamm, B. H. (2010). The Concise ProQOL Manual. Pocatello, ID: Author.
Stevens, J. P. (1992). Applied multivariate statistics for the social sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80, 99–103. doi:10.1207/S15327752JPA8001_18
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson.
Wachter, C. A., Clemens, E. V., & Lewis, T. F. (2008). Exploring school counselor burnout and school counselor involvement of parents and administrators through an Adlerian theoretical framework. Journal of Individual Psychology, 64, 432–449.
Weston, R., & Gore, P. A., Jr. (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34, 719–751. doi:10.1177/0011000006286345
Whiston, S. C., Tai, W. L., Rahardja, D., & Eder, K. (2011). School counseling outcome: A meta-analytic examin-ation of interventions. Journal of Counseling & Development, 89, 37–55.
doi:10.1002/j.1556-6678.2011.tb00059.x
Wilkerson, K. (2009). An examination of burnout among school counselors guided by stress-strain-coping theory. Journal of Counseling & Development, 87, 428–437. doi:10.1002/j.1556-6678.2009.tb00127.x
Wilkerson, K., & Bellini, J. (2006). Intrapersonal and organizational factors associated with burnout among school counselors. Journal of Counseling & Development, 84, 440–450.
doi:10.1002/j.1556-6678.2006.tb00428.x
Wilkerson, K., Pérusse, R., & Hughes, A. (2013). Comprehensive school counseling programs and student achievement outcomes: A comparative analysis of RAMP versus non-RAMP schools. Professional School Counseling, 16, 172–184. doi:10.5330/PSC.n.2013-16.172
Young, M. E., & Lambie, G. W. (2007). Wellness in school and mental health systems: Organizational influences.
The Journal of Humanistic Counseling, 46, 98–113. doi:10.1002/j.2161-1939.2007.tb00028.x
Patrick R. Mullen, NCC, is an Assistant Professor at the College of William and Mary. Daniel Gutierrez, NCC, is an Assistant Professor at the University of North Carolina – Charlotte. Correspondence can be addressed to Patrick Mullen, School of Education, P.O. Box 8795, College of William & Mary, Williamsburg, VA 23188, prmullen@wm.edu.
